Researchers at John Hopkins are training AI algos to recognize and warn of cardiac events, COVID being the culprit.

The National Science Foundation has issued a grant of $195,000 to researchers at John Hopkins University to study the use of AI algorithms for detecting heart disease amongst COVID-19 patients. There is increasing evidence that COVID-19 damages a patient’s cardiovascular system. Patients suffer “cardiac events” such as heart attacks, abnormal heartbeats, and death. (Business Standard)

COVID-19 and the heart

According to The Harvard Gazette, COVID patients with pre-existing heart disease, whether diagnosed or not, are at a greater risk of severe cardiovascular and respiratory complications from COVID-19. Again, previously healthy patients with no history of heart ailments may develop fulminant inflammation of the heart muscle as a result of the coronavirus directly infecting the heart.

“As a clinician, major knowledge gaps exist in the ideal approach to risk stratify COVID-19 patients for new heart problems that are common and may be life-threatening,” said Allison G. Hays, Associate Professor at the Johns Hopkins University. “These patients have varying clinical presentations and a very unpredictable hospital course,” Hays added.

Developing a predictive system

Can AI recognize the signs of imminent cardiac complications due to the virus?

The project researchers will use data from more than 300 COVID-19 patients who received treatment for COVID-19 at John Hopkins. This data will train an AI algorithm using machine learning.

The data covers electrocardiogram (ECG), cardiac-specific laboratory tests, vital signs like heart rate and oxygen saturation, and imaging data such as CT scans, and echocardiography.

After training the algorithm the researchers will test the algorithm at John Hopkins, proximate hospitals, and in New York City.

“This project will provide clinicians with early warning signs and ensure that resources are allocated to patients with the greatest need,” said Natalia Trayanova, a professor at the Johns Hopkins University, and the project’s principal investigator.

Ultimately, the model will generate a predictive risk score that can warn of an adverse cardiac event at least 24 hours in advance.

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